System for managing data collection processes

ABSTRACT

A system and process for managing data collection processes is disclosed. An apparatus that incorporates teachings of the present disclosure can include, a data collection system having a controller element that assigns to each of the processes a query interval according to a priority level of the data collection process for requesting use of processing resources, receiving one or more requests from the processes, once per respective query interval, for use of at least a portion of available processing resources, releases at least a portion of the available processing resources to a requesting one of the processes when the use of the available processing resources exceeds a utilization threshold. Additional embodiments are disclosed.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.12/625,310 filed Nov. 24, 2009, which is a continuation of U.S. patentapplication Ser. No. 11/677,546 filed Feb. 21, 2007, both of which areincorporated herein by reference in their entirety.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to data management systems, andmore specifically to a system for managing data collection processes.

BACKGROUND OF THE DISCLOSURE

Computing system performance is generally dependent on the propermanagement of the execution of software processes (also referred to asjobs, or tasks). In small scale systems, managing the execution ofprocesses is relatively straightforward and can be normally accomplishedthrough a first-in, first-out (FIFO) method for organizing saidprocesses. In small scale systems, the number of processes is typicallysmall and the computing resources required are nominal. Consequently,the amount of delay in completing a process is insignificant and doesnot adversely affect system performance.

In large scale systems with processes requiring significant computingresources such as in a data collection system which retrieves, storesand manages large databases for data mining purposes, a queuing methodsuch as a FIFO can substantially impact throughput performance ofcritical processes when all processes including non-critical processesare given equal access to computing resources.

A need therefore arises for a system for managing data collectionprocesses.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an exemplary embodiment of a system for managing datacollection;

FIG. 2 depicts an exemplary method operating in portions of the system;

FIG. 3 depicts an illustration for calculating a query interval for datacollection processes operating in the system; and

FIG. 4 depicts an exemplary diagrammatic representation of a machine inthe form of a computer system within which a set of instructions, whenexecuted, can cause the machine to perform anyone or more of themethodologies disclosed herein.

DETAILED DESCRIPTION OF THE DRAWINGS

Embodiments in accordance with the present disclosure provide for asystem for managing data collection processes.

In a first embodiment of the present disclosure, a data collectionsystem can have a controller element that assigns a priority level toeach of a plurality of processes for collecting data in the datacollection system, assigns a query interval for requesting use ofavailable processing resources of the controller element to each of theplurality of processes according to their respective assigned prioritylevel, prompts each of the plurality of pending processes according totheir respective query interval to request use of at least a portion ofavailable processing resources of the controller element, releases atleast a portion of the available processing resources of the controllerelement to a requesting one of the plurality of processes when theutilization of available processing resources exceeds a utilizationthreshold, and modifies the query interval of one or more of theplurality of processes after occurrence of a triggering event.

In a second embodiment of the present disclosure, a computing device caninclude a controller element that generates data and prompts a datacollection system to transfer data between the data collection systemand the computing device. The data collection system can assign apriority level to each of a plurality of processes, where at least oneof the plurality of processes comprises a process associated withtransferring data between the computing device and the data collectionsystem, compute a query interval for each of the plurality of processesaccording to their respective assigned priority level and availableprocessing resources of the data collection system, release at least aportion of the available processing resources of the data collectionsystem to a requesting one of the plurality of processes according torelease criteria, and re-compute the query interval of one or more ofthe plurality of processes upon occurrence of one or more triggeringevents.

In a third embodiment of the present disclosure, a computer-readablestorage medium in a data collection system is provided, where thestorage medium can include computer instructions for assigning apriority level to each of a plurality of processes, computing a queryinterval for each of the plurality of processes according to theirrespective assigned priority level and available processing resources,prompting one or more of the plurality of processes according to theirrespective query interval to request use of at least a portion of theavailable processing resources, releasing at least a portion of theavailable processing resources to a requesting one of the plurality ofprocesses according to release criteria, and re-computing the queryinterval of one or more processes upon occurrence of one or moretriggering events.

FIG. 1 depicts an exemplary embodiment of a system 100 for managing datacollection. The system 100 can comprise one or more remote computingdevices (RCDs) 102 accessible by a data collection system (DCS) 104 thatmanages execution of data collection processes, such as uploading anddownloading of data from the RCDs 102. In the system 100, datacollection processes can be submitted to the DCS 104 from one or moreuser interfaces or terminals 106. Additionally, the DCS 104 can bemonitored and controlled via an administrator interface 108 thatprovides administrator access to the DCS 104. Although shown as a singlecomputing system, the DCS 104 can be represented as centralized ordecentralized computing devices.

Similarly, RCDs 102 can also operate as single computing systems or ascentralized or decentralized computing devices. For example, in a system100 for managing data collections processes for a telecommunicationsnetwork, RCDs 102 can comprise order management systems, IP MultimediaSubsystems, customer premise equipment provisioning systems, voicemailsystems, or address book systems just to mention a few. Othertelecommunication systems not described herein that can operate as RCDs102 for the purpose of data collection processing can also be applied tothe present disclosure.

A DCS 104 operating in the system 100 can comprise a mass storage system110 and a controller element 112. The mass storage system 110 canutilize common storage technologies (e.g., hard disk drives, flashmemory, etc.) to store data from one or more of the RCDs 102 in one ormore databases. The controller element 112 can utilize common computingtechnologies (e.g., desktop computer, server, etc.) to manage use ofavailable processing resources 114 (e.g., a plurality of common slidablecomputing cards inserted or removed from a shelf) of the DCS 104 forexecuting one or more data collection processes in a list of pendingprocesses 116 stored by the DCS 104.

FIG. 2 depicts an exemplary method 200 operating in portions of thesystem 100. Method 200 begins with step 202 in which a request forexecution of one or more data collection processes is submitted throughthe user interface 106 to the DCS 104. Data collection processes cancorrespond to software processes that transfer data between RCDs 102 andthe DCS 104. Data collection processes can also be adapted for otherapplications. For example, system 100 can be used for generatingdatabases for data mining purposes and a data collection process definedby a user request can be configured to cause the DCS 104 to retrievedata from the RCDs 102 and process said data with pattern recognitionmethods that can detect demographic and/or psychographic data patterns.Data collection process can be configured to segment the data beingtransferred between the DCS 104 and the RCDs 102. For example, datacollected by a data collection process can be segmented according tocustomer information, product information, or service information, eachof which can be processed by the aforementioned pattern recognitionmethod for data mining purposes.

In response to the request submitted in step 202, the DCS 104 can addthe one or more associated data collection processes to the list ofpending processes 116. In step 204 the DCS 104 can assign each of thesedata collection processes a priority level. The DCS 104 can assign apriority level in several ways. For example, the priority level can bebased on the type of user submitting the process request. That is, datacollection processes submitted by some users can receive a higherpriority than processes submitted by other users. A higher prioritylevel can also be assigned by the DCS 104 to data collection processesaccording to parameters specified by the user when creating the request.

For example, a parameter can be included with a request defining acritical date by which an associated data collection process must becompleted. The DCS 104 can also assign a priority level based on aproximity to the critical date. The DCS 104 can also use a combinationof parameters included in the request to assign a priority level to adata collection process. For example, a first data collection processassociated with a request including a critical date and submitted by alow priority user can be assigned the same priority level as a seconddata collection process associated with a request identifying nocritical date but submitted by a higher priority user.

The priority level for a data collection process can also be assigned byway of the administrator interface 108. For instance, in step 206, anadministrator can manually assign a priority level to a data collectionprocess through an administrator interface 108 having access to the DCS104. The administrator can also override a priority level alreadyassigned to the data collection process during step 204. This providesthe administrator with the ability to manually manage the list ofpending data collection processes 116 and allow one or more datacollection processes to be executed sooner or later than it wouldnormally be processed by a resource 114 of the DCS 104.

In prior art systems, a data collection system manages its processingresources by releasing at least a portion of its available processingresources in response to a request by a data collection process foravailable processing resources. Method 200 instead manages the releaseof processing resources 114 to data collection processes by computing aquery interval in step 212 that can vary according the priority level ofthe pending data collection process 116 and the amount of processingresources 114 available in the DCS 104. In preparation for calculatingthe query interval for a particular data collection process, the DCS 104can be configured to determine, in step 208, how many other datacollection processes in the list of pending data collection processes116 have been assigned a higher priority level than the data collectionprocess in question. Concurrently or consecutively, in step 210, the DCS104 can also be configured to determine the amount of the processingresources 114 available to be released to the pending data collectionprocesses 116.

Steps 208-210 serve to ensure sufficient processing resources areavailable for both data collection processes and other activities of theDCS 104 such as data mining. To assure that data mining activities havesufficient processing resources, the DCS 104 can be configured todetermine whether too many data mining activities are being interruptedby data collection processes and therefrom respond by reducing theprocessing resources 114 released to data collection processes. Thisforced reduction, reserves a portion of the processing resources 114 fordata mining and other related activities.

Conversely, if the DCS 104 determines that data mining and other relatedactivities are not being impacted by data collection processes, theamount of processing resources 114 made available to pending datacollection processes 116 can be increased. The DCS 104 can continue toincrease the processing resources 114 until an adequate load balance isachieved between processing resources assigned to data collectionprocesses and those reserved for data mining processes. The DCS 104 canalso monitor and control its workload by comparing the total utilizationof the processing resources 114 to one or more utilization thresholdvalues, which can automatically trigger the DCS to reduce or increasethe amount of processing resources 114 released to pending datacollection processes 116. Such threshold values can vary according totime of day or according to any other parameter suitable to the presentdisclosure. Alternatively, an administrator can manually adjust theamount of processing resources 114 released to data collection processesto achieve similar results.

With these principles in mind, the DCS 104 calculates in step 212 aquery interval for each data collection process based on the amount ofprocessing resources 114 available to be used by data collectionprocesses determined in step 210 and the total number of other datacollection processes assigned a higher priority than the data collectionprocess determined in step 208. Because the priority of data collectionprocesses can vary, the query interval of data collection processes canalso vary.

A variable query interval such as described can result in an increasedprobability that the DCS 104 will release an available resource from thelist of processing resources 114 to data collection processes assigned ahigher priority level. For example, a data collection process assigned alow priority level can be assigned a longer query interval by the DCS104 causing it to query (or request) a processing resource lessfrequently. In contrast, a data collection process assigned a highpriority level can have a shorter query interval causing it to query foran available processing resource more frequently.

Because the amount of processing resources 114 available can vary overtime, the more frequently a data collection process can query the DCS104 to release at least a portion of the processing resources 114 themore likely the query will coincide with a time when the amount ofprocessing resources available are sufficient to execute the requestingdata collection process. It follows also that data collection processesthat query the DCS 104 more frequently will have a higher probability ofbeing executed prior to data collection processes querying the DCS lessfrequently (i.e. those having a longer query interval).

A method for calculating the query interval is illustrated in FIG. 3. Inthis illustration, a query interval can calculated from the sum of adefault query interval (A), a system delay time (B) associated with theamount of processing resources 114 available and an additional delaytime (e) accounting for any other data collection processes in the listof pending processes 116 assigned a higher priority level.

The default query interval (A) used in the exemplary calculation of FIG.3 can be an initial query interval that can be assigned to every datacollection process as a variable delay. The initial query interval canbe a static value selected by an administrator or a default valueassigned by the DCS 104. The default query interval value (A) can alsobe calculated from an average of the query intervals assigned to thedata collection processes already in the list of pending processes 116or according to a measure of resources actively engaged by the DCS 104.For illustration purposes only, the default query interval has been setto 30 seconds according to any of the aforementioned embodiments.

The system delay time (B) can be used to account for variations in theamount of processing resources 114 available to pending data collectionprocesses 116. The system delay time (B) manages the workload of the DCS104 by adjusting the query interval for each data collection process inthe list of pending processes 116, thus increasing or decreasing theprobability of execution of said pending processes. The calculatedsystem delay time (B) can serve as a variable delay. The system delaytime (B) can be a function of the amount of expected availableprocessing resources (X) and the amount of currently availableprocessing resources (Y). In particular, the system delay time (B) canbe expressed as the difference between the expected and currentlyavailable processing resources (X-Y).

Said difference (X-Y) can be scaled by a system scaling factor such as agiven an amount of time. The system scaling factor in the presentillustration is given as 50 seconds. Consequently, if there are 3normally available processing resources (X=3) and 2 currently availableprocessing resources (Y=2), a system delay time is determined to beequal to (X−Y)(50 s)=(3−2)(50 s) or 50 seconds. It follows that as theamount of processing resources increases, the query interval isdecreased by the system delay time (B) and the probability of executionof a data collection process increases. Similarly, as the amount ofavailable processing resources is decreased, the query interval isincreased by the system delay time (B) and the probability of executionof a data collection process is decreased.

Along with the system delay time (B), the additional delay time (C) canbe used to individually adjust the query interval of each datacollection process according to the number of other data collectionprocesses assigned a higher priority. The calculated additional delaytime (C) can also serve as a variable delay. The additional delay time(C) can be a function of the total number of other data collectionprocesses assigned a higher priority level (Z) scaled by an additionalprocess scaling factor based on a fixed given time such as 15 s. Forexample, if there are 5 other data collection processes assigned ahigher priority level (Z=5), the additional delay time would be equal to(Z)(15 s)=(5)(15 s) or 75 seconds, which increases the query intervaland decreases the probability the data collection process will beexecuted. Thus for a given data collection process as the number ofother data collection processes having a higher priority leveldecreases, the additional delay time (C) also decreases, therebyincreasing the probability the data collection process will be executed.

Based on the default query interval (A), the system delay time (B), andthe additional delay time (C), the DCS 104 can calculate the queryinterval as a variable factor (query interval=A+B+C). For example,according to the illustration of FIG. 3 the query interval would be thesum of the default query interval (A=30 s), the system delay time (B=50s), and the additional delay time (C=75 s) or A+B+C=30 s+50 s+75 s=155seconds.

The query interval can be further refined to improve performance of theDCS 104. One method is to assign data collection processes havingsimilar calculated query intervals the same query interval, such ascalculated query interval in a specific range. For example, if a firstgroup of data collection processes have a calculated query intervalbetween 1 and 40 seconds, the DCS 104 can assign all the processes inthe first group a first common query interval, such as 20 seconds. Asecond group of data collection processes in the range of 40 to 90seconds may be similarly assigned a second common query interval, suchas 60 seconds, and so forth. Assigning multiple data collectionprocesses the same query interval can reduce the need for the overheadin performing the calculations illustrated in FIG. 3.

Once the query interval for a data collection process is calculated instep 212, the data collection process can query in step 214 the DCS 104at its prescribed query interval to determine whether processingresources 114 of the DCS 104 are available. In step 218 the DCS 104 canbe configured to determine whether the amount of available processingresources 114 are sufficient for executing the data collection processand at least one other data collection process in the list of pendingprocesses 116 assigned a higher priority level. This step assures thatat least one higher priority data collection process can be executedthereafter. Once the DCS 104 determines that the amount of availableprocessing resources 114 is sufficient for executing the data collectionprocess in question in step 220, the DCS releases in step 222 at least aportion of the available processing resources 114 to said datacollection process. If it is determined in step 220 that none of theprocessing resources 114 can be released to the requesting datacollection process, the requesting process will return to a pendingstate in which it will not submit another request for resources untilpassage of another query interval in step 214.

It should be noted that the query interval does not have to be staticand can be modified or re-computed over time based on the occurrence ofa triggering event. For example, in step 224, the DCS 104 can beconfigured to determine whether a triggering event has occurred thatrequires recalculation of the query interval for data collectionprocesses in the list of pending processes 116. Various types of eventscan be monitored by the DCS 104 to trigger recalculation of the queryinterval. A first type of event can be the passage of a fixed intervalof time, such as 5 minutes or some other suitable interval of time. Asecond type of event can be the workload or utilization resources of theDCS 104 exceeding a utilization threshold. To determine such an eventhas occurred, the DCS 104 can be configured to determine whether thetotal utilization of the processing resources 114 has exceeded autilization threshold value. For example, if the DCS 104 determines that90% of the processing resources 114 are being used and the utilizationthreshold is only 70%, recalculation of the query interval can betriggered for all of data collection processes to reduce the workload onthe DCS.

A third type of triggering event can be a change in the total number ofdata collection processes in the list of pending processes 116 or achange in the total number of data collection processes in the list ofpending processes assigned a particular or range of priority levels.Each of these events can be assigned a quantity threshold value.Exceeding anyone of these quantity thresholds can signal the DCS 104that a query interval recalculation is necessary to avoid having toomany processes in queue.

Once the DCS 104 detects that a triggering event has occurred in step226 then the process of determining the query interval, steps 208-212,can be repeated to determine a new query interval for one or more datacollection processes in the list of pending processes. By repeatingsteps 208, 210 and 212, the new query interval can take into account theaforementioned changes in the number and priority level of the datacollection processes in the list of pending processes 116 and thecurrent workload of the DCS 104. If the DCS 104 does not detect that atriggering event has occurred in step 226, then the query interval canbe left unchanged. The various steps in method 200 can be repeated fromstep 208 for each of the data collection processes in the list ofpending processes 116 until all processes in the list of pendingprocesses 116 are executed.

Method 200 provides a means to more efficiently assign processingresources of the DCS 104 to pending data collection processes. It wouldbe apparent by these examples that several modifications can be appliedto the present disclosure without departing from the scope of the claimsstated below. Accordingly, the reader is directed to the claims sectionfor a fuller understanding of the breadth and scope of the presentdisclosure.

FIG. 4 depicts an exemplary diagrammatic representation of a machine inthe form of a computer system 400 within which a set of instructions,when executed, can cause the machine to perform anyone or more of themethodologies discussed above. In some embodiments, the machine operatesas a standalone device. In some embodiments, the machine can beconnected (e.g., using a network) to other machines. In a networkeddeployment, the machine can operate in the capacity of a server or aclient user machine in server-client user network environment, or as apeer machine in a peer-to-peer (or distributed) network environment.

The machine can comprise a server computer, a client user computer, apersonal computer (PC), a tablet PC, a laptop computer, a desktopcomputer, a control system, a network router, switch or bridge, or anymachine capable of executing a set of instructions (sequential orotherwise) that specify actions to be taken by that machine. It will beunderstood that a device of the present disclosure includes broadly anyelectronic device that provides voice, video or data communication.Further, while a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to performanyone or more of the methodologies discussed herein.

The computer system 400 can include a processor 402 (e.g., a centralprocessing unit (CPU), a graphics processing unit (GPU, or both), a mainmemory 404 and a static memory 406, which communicate with each othervia a bus 408. The computer system 400 can further include a videodisplay unit 410 (e.g., a liquid crystal display (LCD), a flat panel, asolid state display, or a cathode ray tube (CRT)). The computer system400 can include an input device 412 (e.g., a keyboard), a cursor controldevice 414 (e.g., a mouse), a disk drive unit 416, a signal generationdevice 418 (e.g., a speaker or remote control) and a network interfacedevice 420.

The disk drive unit 416 can include a machine-readable medium 422 onwhich can be stored one or more sets of instructions (e.g., software424) embodying anyone or more of the methodologies or functionsdescribed herein, including those methods illustrated above. Theinstructions 424 can also reside, completely or at least partially,within the main memory 404, the static memory 406, and/or within theprocessor 402 during execution thereof by the computer system 400. Themain memory 404 and the processor 402 also can constitutemachine-readable media.

Dedicated hardware implementations including, but not limited to,application specific integrated circuits, programmable logic arrays andother hardware devices can likewise be constructed to implement themethods described herein. Applications that can include the apparatusand systems of various embodiments broadly include a variety ofelectronic and computer systems. Some embodiments implement functions intwo or more specific interconnected hardware modules or devices withrelated control and data signals communicated between and through themodules, or as portions of an application-specific integrated circuit.Thus, the example system can be applicable to software, firmware, andhardware implementations.

In accordance with various embodiments of the present disclosure, themethods described herein are intended for operation as software programsrunning on a computer processor. Furthermore, software implementationscan include, but not limited to, distributed processing orcomponent/object distributed processing, parallel processing, or virtualmachine processing can also be constructed to implement the methodsdescribed herein.

The present disclosure contemplates a machine readable medium containinginstructions 424, or that which receives and executes instructions 424from a propagated signal so that a device connected to a networkenvironment 426 can send or receive voice, video or data, and tocommunicate over the network 426 using the instructions 424. Theinstructions 424 can further be transmitted or received over a network426 via the network interface device 420.

While the machine-readable medium 422 is shown in an example embodimentto be a single medium, the term “machine-readable medium” should betaken to include a single medium or multiple media (e.g., a centralizedor distributed database, and/or associated caches and servers) thatstore the one or more sets of instructions. The term “machine-readablemedium” shall also be taken to include any medium that is capable ofstoring, encoding or carrying a set of instructions for execution by themachine and that cause the machine to perform anyone or more of themethodologies of the present disclosure.

The term “machine-readable medium” shall accordingly be taken toinclude, but not be limited to: solid-state memories such as a memorycard or other package that houses one or more read-only (non-volatile)memories, random access memories, or other re-writable (volatile)memories; magneto-optical or optical medium such as a disk or tape; andcarrier wave signals such as a signal embodying computer instructions ina transmission medium; and/or a digital file attachment to e-mail orother self-contained information archive or set of archives can beconsidered a distribution medium equivalent to a tangible storagemedium. Accordingly, the disclosure is considered to include anyone ormore of a machine-readable medium or a distribution medium, as listedherein and including art-recognized equivalents and successor media, inwhich the software implementations herein are stored.

Although the present specification describes components and functionsimplemented in the embodiments with reference to particular standardsand protocols, the disclosure is not limited to such standards andprotocols. Each of the standards for Internet and other packet switchednetwork transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP) representexamples of the state of the art. Such standards are periodicallysuperseded by faster or more efficient equivalents having essentiallythe same functions. Accordingly, replacement standards and protocolshaving the same functions are considered equivalents.

The illustrations of embodiments described herein are intended toprovide a general understanding of the structure of various embodiments,and they are not intended to serve as a complete description of all theelements and features of apparatus and systems that might make use ofthe structures described herein. Many other embodiments will be apparentto those of skill in the art upon reviewing the above description. Otherembodiments can be utilized and derived therefrom, such that structuraland logical substitutions and changes can be made without departing fromthe scope of this disclosure. Figures are also merely representationaland may not be drawn to scale. Certain proportions thereof may beexaggerated, while others may be minimized. Accordingly, thespecification and drawings are to be regarded in an illustrative ratherthan a restrictive sense.

Such embodiments of the inventive subject matter can be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose can be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the above description.

The Abstract of the Disclosure is provided to comply with 37 C.P.R.§1.72(b), requiring an abstract that will allow the reader to quicklyascertain the nature of the technical disclosure. It is submitted withthe understanding that it will not be used to interpret or limit thescope or meaning of the claims. In addition, in the foregoing DetailedDescription, it can be seen that various features are grouped togetherin a single embodiment for the purpose of streamlining the disclosure.This method of disclosure is not to be interpreted as reflecting anintention that the claimed embodiments require more features than areexpressly recited in each claim. Rather, as the following claimsreflect, inventive subject matter lies in less than all features of asingle disclosed embodiment. Thus the following claims are herebyincorporated into the Detailed Description, with each claim standing onits own as a separately claimed subject matter.

1. A method comprising: assigning, by a system comprising a processor, arespective priority level to each data collection process of a pluralityof data collection processes for collecting data from one or more remotecomputing devices; assigning, by the system, a query interval to a datacollection process of the plurality of data collection processes atleast in part according to a priority level of the data collectionprocess; receiving, by the system, one or more requests from the datacollection process for use of at least a portion of available processingresources of a controller, wherein the one or more requests are sent bythe data collection process once per the query interval; and releasing,by the system, the portion of the available processing resources of thecontroller to the requesting data collection process when the use of theavailable processing resources exceeds a first utilization threshold. 2.The method of claim 1, wherein the query interval comprises a frequencyfor requesting the available processing resources, and wherein thereleasing of the portion of the available processing resources isrepeated at the query interval.
 3. The method of claim 1, comprisingdetermining, by the system, a computed query interval of the datacollection process according to a sum of a default query interval and anadditional delay time assigned to each one of the plurality of datacollection processes assigned a priority level exceeding the respectivepriority level assigned to the data collection process.
 4. The method ofclaim 3, comprising determining, by the system, a re-computed queryinterval of the data collection process according to a sum of anadditional delay time assigned to each one of one or more of theplurality of data collection processes assigned a priority levelexceeding the respective priority level assigned to the data collectionprocess and a system delay time determined from the available processingresources of the controller.
 5. The method of claim 1, comprisingdetermining, by the system, a computed query interval of the datacollection process according to a sum of a default query interval and asystem delay time determined from the available of processing resourcesof the controller.
 6. The method of claim 1, comprising applying, by thesystem, a release criteria to the release step, wherein the releasecriteria comprises having available processing resources sufficient forprocessing the request from the data collection process and one or moreof the plurality of data collection processes assigned a priority levelexceeding the respective priority level of a requesting one of theplurality of data collection processes.
 7. The method of claim 1,comprising modifying the query interval of the data collection processafter occurrence of a triggering event, wherein the triggering eventcomprises at least one of an interval of time, the use of the availableprocessing resources of the data collection system exceeding the firstutilization threshold, or a quantity of the plurality of data collectionprocesses pending execution exceeding a quantity threshold.
 8. Aprocessor comprising: a memory storing computer instructions; and acontroller coupled to the memory, wherein the controller, responsive toexecuting the computer instructions performs operations comprising:assigning a query interval to a data collection process of a pluralityof data collection processes at least in part according to a prioritylevel of the data collection process, wherein each data collectionprocess of the plurality of data collection processes, responsive tobeing executed, collects data from one or more remote computing devices;receiving one or more requests from the data collection process for useof at least a portion of available processing resources of thecontroller, wherein the one or more requests are sent by the datacollection process once per the query interval; and releasing theportion of the available processing resources of the controller to therequesting data collection process when the use of the availableprocessing resources exceeds a first utilization threshold.
 9. Theprocessor of claim 8, wherein the query intervals comprise a frequencyfor requesting the available processing resources, and wherein thecontroller further performs operations comprising repeating the releasestep at the query interval.
 10. The processor of claim 8, wherein thecontroller further performs operations comprising determining a computedquery interval of the data collection process according to a sum of adefault query interval and an additional delay time assigned to each oneof the plurality of data collection processes having a priority levelexceeding a respective priority level of the data collection process.11. The processor of claim 8, wherein the controller further performsoperations comprising determining a computed query interval of the datacollection process according to a sum of a default query interval and asystem delay time determined from the available of processing resourcesof the controller.
 12. The processor of claim 8, wherein the controllerfurther performs operations comprising determining a re-computed queryinterval of the data collection process according to a sum of anadditional delay time assigned to each one of one or more of theplurality of data collection processes having a priority level exceedinga respective priority level of the data collection process and a systemdelay time determined from the available processing resources of thecontroller.
 13. The processor of claim 8, wherein the controller furtherperforms operations comprising applying a release criteria to therelease step, wherein the release criteria comprises having availableprocessing resources sufficient for processing the request from the datacollection process and one or more of the plurality of data collectionprocesses having a priority level exceeding a respective priority levelof a requesting one of the plurality of data collection processes. 14.The processor of claim 8, wherein the controller further performsoperations comprising modifying the query interval of the datacollection process after occurrence of a triggering event, wherein thetriggering event comprises at least one of an interval of time, the useof the available processing resources of the data collection systemexceeding the first utilization threshold, or a quantity of theplurality of data collection processes pending execution exceeding aquantity threshold.
 15. A computer-readable storage medium, comprisingcomputer instruction, which when executed by a processor cause theprocessor to perform operations comprising: determining a frequency forrequesting available processing resources for each of a plurality ofprocesses according to their respective priority level to generate aquery interval; receiving one or more requests from a process of theplurality of processes for use of at least a portion of the availableprocessing resources, wherein the one or more requests are sent by theprocess once per the query interval; releasing at least a portion of theavailable processing resources to the requesting process according torelease criteria; and re-computing the determined frequency of one ormore processes upon occurrence of one or more triggering events togenerate an updated query interval.
 16. The computer-readable storagemedium of claim 15, wherein the processor further performs operationscomprising repeating releasing steps according to the determinedfrequency.
 17. The computer-readable storage medium of claim 15, whereinthe processor further performs operations comprising re-computing thedetermined frequency of the process according to a sum of a defaultquery interval, an additional delay time assigned to each of one or moreother processes having a respective priority level exceeding arespective priority level assigned to the process, and a system delaytime determined from the available processing resources of theprocessor.
 18. The computer-readable storage medium of claim 15, whereinthe processor further performs operations comprising re-computing thedetermined frequency of the process according to a sum of an additionaldelay time assigned to each one of one or more processes respectivelyhaving a higher priority exceeding a respective priority level of theprocess and a system delay time determined from the available processingresources of the processor.
 19. The computer-readable storage medium ofclaim 15, wherein the release criteria comprises having availableprocessing resources sufficient for executing the requesting process andat least one of the plurality of processes having a priority levelexceeding the respective priority level of the requesting process. 20.The computer-readable storage medium of claim 15, wherein the processorfurther performs operations comprising determining the available ofprocessing resources of the processor, wherein the one or moretriggering events comprise at least one of an interval of time, the useof the available processing resources of the processor exceeding autilization threshold, or a quantity of the plurality of processespending execution exceeding a quantity threshold.